My research focuses on understanding the behavior that emerges in complex adaptive
social systems (CASS).
Understanding the behavior of CASS, composed of interacting,
thoughtful (but perhaps not brilliant) agents, is one of the great
challenges of science. Such systems capture pervasive and important
phenomena, arising in biological, chemical, environmental, economic,
organizational, and political systems. Traditional scientific
approaches to understanding these types of systems have shown only a
limited ability to pry away their secrets. Part of this failure is
attributable to a fundamental limitation of the reductionist approach
to science: reducing systems to components does not imply the ability
to reconstruct them (as anyone who has scrambled an egg innately
understands). While it may seem that because we understand one we
must also understand two, because one and one make two, this is
only true if we also understand and.

Colleagues
and I have applied CASS models to the analysis of a variety of key social
phenomena.
For example, we have explored the dynamics of political platforms in spatial elections, how decentralized institutional mechanisms can be
used to sort agents into coherent groups,
the emergence of cooperation in the iterated Prisoner's Dilemma, strategic choice in simple two-person games, the development of strategic communication,
and bidding behavior in auction markets. To understand the fundamental dynamics of price formation in simple markets, we designed and organized an international computerized double auction tournament in
1988-90 (which was also the first internet-based
auction market---here is the DAT Participant's Manual).
This tournament allowed us to create an "artificial world" of trading agents in which we explored a variety of theoretical and practical issues. All of the above work indicates
the remarkably potential for CASS modeling to advance our
understanding about some of the most fundamental questions in the
social sciences.

For CASS modeling, traditional
tools of scientific theory building, such as mathematical modeling, need to be
supplemented
with computational models that take advantage of recent technological
progress. As the mathematician Stanislaw Ulam pointed out, ``[T]he
use of computers seems thus not merely convenient, but absolutely
essential...I believe that the experience gained as a result of
following the behavior of such processes will have a fundamental
influence on whatever may ultimately generalize or perhaps even
replace in mathematics our present exclusive immersion in the formal
axiomatic method.''
Using computational methods, previously inaccessible, yet fundamental, questions are now becoming amenable to analysis.

Complementing the above work, I have also pursued experimental approaches to understanding
human cooperation and
altruism. In some recent experiments we have shown that the altruistic choices of human
subjects are economically rationalizable in the sense that they could be accounted for
by a standard model of economic choice. We also found that while the altruistic
preferences of
subjects fall into three distinct classes, there is a large
degree of heterogeneity in their preferences.

At the conclusion of Origins of the Species, Darwin remarked
that ``[T]here is grandeur in this view of life, with its several
powers, having been originally breathed into a few forms or into
one...from so simple a beginning endless forms most beautiful and most
wonderful have been, and are being, evolved.''
Through a remarkable
convergence of ideas, technology, and scientific and engineering
imperative, we now stand ready to exploit this ``view of life'' in a
quest to understand and control complex adaptive social systems.

Complex Adaptive Systems:
An Introduction to Computational Models of
Social Life

Scott Page (Michigan) and I have written a book on complex adaptive social systems
(Complex Adaptive Systems, Princeton University
Press, 2007).
In the book, we derive some fundamental examples and principles
of complex adaptive social systems using a simple set of related
models. Models composed of thoughtful agents fundamentally differ from the
typical interacting particle systems that have been widely discussed in
the past. The simple models we develop allow us to
to illuminate, and often correct, some of the key principles that have been
discussed over the past two decades. Finally, we take the opportunity
to outline more fully the foundations necessary for successful
computational modeling in this area.

The Ghost in the Machine

James Engle-Warnick (McGill) and I are developing sound statistical
methods for divining the underlying "strategic programs" employed by
human subjects in experimental games. The goal here is to have a succinct characterization of
actual human strategic choice, and then use this knowledge to better understand
and predict behavior in such situations.

Neutral Technological Networks: The Value of Muddling Through

This research applies ideas about
the impact of neutral networks on evolutionary dynamics in biology to
the problem of technological innovation. Theoretically, we find that there
may be considerable value in firms "muddling through" technological spaces, as
such muddling allows them to achieve superior outcomes that under more traditional
models would not have been attainable. One example of this process is in
software design, where recent notions of
"refactoring" implicitly attempt to exploit the value of
neutral networks.
[abstract][pdf]

Discovering Novel Chemotherapies

There is good reason to think that cocktails composed of
a variety of chemotherapy drugs might be a key means by which to
fight diseases like cancer. Unfortunately, discovering effective
cocktails is difficult given the underlying combinatorics.
If you have, say, twenty drugs that you could include in the mix,
you can create over one million different cocktails. Alas, you can
only screen around twenty or so cocktails every few days (laboratory
biology is slow and hard), so there is no way to exhaustively search
all of the possible combinations. To surmount this problem, we employ
a nonlinear search algorithm to direct the discovery of the cocktails.
Our preliminary results, focusing on
lung carcinomas, are quite promising---the algorithm quickly found a
very effective cocktail that is promising enough to begin more elaborate
trials.
[abstract][pdf]

Giving According to GARP

This work uses laboratory studies of human subjects to show that their altruistic
choices are economically rational, in the sense that such choices could result from
the rational maximization of a well-behaved (in the economic sense) utility function
over giving to self and to others. Here is a variant of the
basic experiment...are you
a rational altruist?
[abstract][pdf]

One of the most powerful tools arising from complex systems research
is a set of computational techniques that allow a much wider range of
models to be explored. Using these tools, worlds of heterogeneous, adaptive
agents can interact in a dynamic environment subjected to various
limits of time and space. Having the ability to investigate new
theoretical worlds obviously does not imply any kind of scientific
necessity or validity---these must be earned by carefully considering
the ability of the new models to help us understand and predict the
questions that we hold most dear.

Over the last decade or so, computational modeling is beginning to
gain a foothold in theoretical social science. Like any theoretical
tool, great care and effort is required to create high quality models
using computational methods. Here are some links focused on
computational modeling in the social sciences
(including our graduate workshop). Part of our
book on complex adaptive social systems will also
be devoted to this topic.

Ken Kollman, John H. Miller, and Scott E. Page,
``A Simplified Framework for Analyzing the Behavior of
Political Institutions,'' 1999.

John H. Miller, ``A Genetic Model of Adaptive Economic Behavior,'' University
of Michigan working paper, 1986.
This was, to my knowledge, the first paper in economics to use genetic
algorithms.
[abstract][pdf]

Kollman, John H. Miller, and Scott Page,
``Consequences of Nonlinear Preferences in a Federal Political System,''
in Diana Richards (ed.), Political Complexity: Nonlinear Models
of Politics, University of Michigan Press, Ann Arbor, MI (2000):23--45.